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All Journal Jurnal Ilmu Komputer dan Agri-Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Bulletin of Computer Science Research Mejuajua
Rossy Nurhasanah
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Engineering Languange, Linguistic, Communication & Media Public Health Other

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Penerapan Probabilistic Neural Network pada Klasifikasi Patogen Daun Bibit Jabon Berdasarkan Ciri Morfologi Spora Melly Br Bangun; Yeni Herdiyeni; Elis Nina Herliyana; Rossy Nurhasanah
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.325

Abstract

The aim of this research is to clasify pathogen of Jabon’s leaf seedling based on spora morphological features using Probabilistic Neural Network classifier. Three types of pathogen to be classified are Colletotrichum sp., Curvularia sp., and Fusarium sp.. The methodologies used are data acquisition using optilab camera microscope to obtain microscopic image data , preprocessing (grayscale, median smoothing, thresholding Otsu, region filling, median smoothing and dilate), morphology feature extraction (area, perimeter, area convex, convex perimeter, compactness, solidity, convexity and roundness), Probabilistic Neural Network classification, and evaluation. The basic morphological characteristics consisting of area, perimeter, convex area, convex perimeter, and derived morphological characteristics consisting of compactness, solidity, convexity and roundness. The experimental results of the morphological feature extraction showed that the compactness and roundness characteristics can be used to identify the three types of pathogens because with these characteristics each class of pathogen is separate. Testing for this research was carried out using 150 test data from three classes of objects from the dataset, namely class 1 (Colletotrichum sp.), class 2 (Curvularia sp.), and class 3 (Fusarium sp.). Then the results of pathogen classification using the application of the PNN algorithm in testing this research obtained an average accuracy value of 86.8% with a proportion of training data and test data of 80:20. The results of the PNN classification on 150 test data were that there were 36 data classified into Colletotrichum sp., 44 data classified into Curvularia sp., and 50 data classified into Fusarium sp. Further research could be done with the identification of digital microscopic images without cropping and systems that could clasify a colony image of pathogens clearly.
Topic Modelling on Beauty Product Reviews Using Latent Dirichlet Allocation Ade Sarah Huzaifah; Huzaifah, Ade Sarah; Rossy Nurhasanah; R. A. Fattah Adriansyah
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 1 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.1.119-131

Abstract

In contemporary society, beauty products have become essential, particularly for women. With their growing popularity, online review platforms now provide extensive information on product trends, customer satisfaction, and performance. However, the sheer volume of available reviews presents challenges in drawing meaningful conclusions. To address this, topic modeling techniques such as Latent Dirichlet Allocation (LDA) have been widely employed in text mining and information retrieval. LDA is a probabilistic model capable of uncovering latent structures within textual data and identifying similarities across documents. Recent studies suggest that topic modeling of product reviews in the cosmetics industry can yield valuable insights into consumer perceptions and product attributes. This study aims to identify thematic patterns in customer reviews of ten facial cleanser brands sourced from the Female Daily website. The research methodology consists of five main stages: data collection, preprocessing, topic modeling using LDA, visualization, and topic interpretation. The results reveal that Topic 2, which highlights preferred product advantages, is the most frequently discussed, accounting for 48.5% of the total reviews. Topic 1, which focuses on the effects of products on acne-prone skin, constitutes 38%, while Topic 3, emphasizing products with natural ingredients, makes up 13.5% of the reviews. These findings can assist businesses in developing products that align more closely with consumer preferences. Moreover, they support prospective buyers in making informed purchasing decisions by enhancing their understanding of product attributes based on user experiences
COMMUNITY-BASED PRODUCTION OF LIQUID SMOKE AS A NON-CARCINOGENIC FISH PRESERVATIVE: - Taufik, Muhammad; Boby Cahyady; Rossy Nurhasanah; Masruroh, Pingkan; Syahputra, Muhammad Rizky; Azzahrah, Nabilah Azka; Zul Alfian; Mariany Razali
Mejuajua: Jurnal Pengabdian pada Masyarakat Vol. 5 No. 2 (2025): Desember 2025
Publisher : Yayasan Penelitian dan Inovasi Sumatera (YPIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/mejuajuajabdimas.v5i2.297

Abstract

The growing demand for safe fish preservatives has encouraged the use of liquid smoke as an alternative to high-temperature processing, which may form carcinogenic compounds. This community service program focused on producing coconut-shell-based liquid smoke through pyrolysis followed by distillation to obtain grade-2 liquid smoke. Acetic acid content was determined using alkalimetry with 0.1 N NaOH. The results showed an increase in acetic acid concentration from 1% to 6.5%, meeting the grade-2 standard of SNI 8985:2021. Application through 20-minute immersion preserved fish quality for up to three days without generating carcinogenic properties. Beyond product development, the program strengthened local socio-economic conditions by providing training, empowering residents to produce liquid smoke independently, and opening new micro-enterprise opportunities. This demonstrates that community-based production can supply safe, standardized preservatives while enhancing economic resilience in Manunggal Village.
Optimization of HIV/AIDS Classification Using the SMOTE Technique and CatBoost Algorithm Annisa Fadhillah Pulungan; Chairil Umri; Rossy Nurhasanah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.18134

Abstract

Despite various mitigation efforts, Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS) remains a significant public health issue with widespread impacts in Indonesia. One of the challenges in HIV/AIDS classification using machine learning is data imbalance, where the number of HIV cases is smaller than Non-HIV cases. The aim of this study is to analyze the performance of the CatBoost algorithm in classification tasks and to evaluate the impact of the Synthetic Minority Oversampling Technique (SMOTE) on improving model performance in imbalanced datasets. The research method involves applying the CatBoost algorithm to the original dataset as well as to data that has been processed using SMOTE-based oversampling. Furthermore, model performance is evaluated using Precision, Recall, F1-Score, and Precision-Recall Area Under Curve (PR-AUC) metrics. The SMOTE + CatBoost model achieved an accuracy of 95%, precision of 93%, recall of 92%, F1-Score of 93%, and PR-AUC of 0.953, all of which are higher than those of the CatBoost Baseline model. In addition, the number of undetected HIV cases was reduced from 28 to 13 cases. The findings indicate that the integration of SMOTE with the CatBoost algorithm improves model performance, resulting in better classification outcomes on imbalanced datasets compared to the CatBoost Baseline, and potentially supports a more effective HIV/AIDS early detection system.
Co-Authors Ade Sarah Huzaifah Annisa Fadhillah Pulungan Azzahrah, Nabilah Azka Boby Cahyady Chairil Umri Elis Nina Herliyana Huzaifah, Ade Sarah Mariany Razali Masruroh, Pingkan Melly Br Bangun MUHAMMAD TAUFIK R. A. Fattah Adriansyah Syahputra, Muhammad Rizky Zul Alfian